Expert View: Do You Need To Understand Data To Visualize It?

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The common belief is that data visualizations are simply about taking information and making it look nice in a picture, graph, or interactive format. However, is this true? Can you simply take a load of conclusions that somebody else has drawn from an analysis and make a nice design around it?

It is not a controversial thing to say that the best visualizations are the ones that both look visually appealing and simple, something that a designer always looks to achieve. So could it simply be that all a data visualization program needs are a load of data scientists and somebody who knows their way around design? It is difficult to see, given the often complex nature of data, the necessity for the data to be displayed correctly and the struggle that many companies have in finding the best way to communicate complex subjects in a simple way.

To find out, we asked some of the top minds in the industry whether they felt people needed to have an inherent understanding of the data they were visualizing and the answers, as you can see, were fairly unanimous:

Doug Ireland, VP Finance/Controller at Prezi

Yes, but the process of visualization is also a process of making sense of the data. This process can be broken down into multiple questions that can then help even data novices. What type of visualization to use in a given context or for a given message, for example, or how to tell the story of change or contrast in data – there are many questions that tool providers can help with to make effective data visualization accessible to novices, too.

I am an absolute advocate for understanding data prior to visualizing it. Too often, I have seen visualizations based on data that is either flawed or displayed out of context, so that it doesn't really make sense in the final visualization. This is especially critical in a business context, where your end users may be somewhat familiar with the data. Making obvious mistakes interpreting or displaying the data can be damaging to your credibility.

Gabi Steele, Data Visualization Specialist at The Washington Post

Abon Chaudhuri, Senior Applied Researcher, Walmart Labs

Absolutely. An effective visualization results from a great deal of curiosity and exploration. The data we come across are often noisy, sparse, biased, incomplete, or irrelevant. Each of these problems should be investigated before finalizing the visualization. For example, if we have to visualize a 2D matrix, we may want to employ different techniques depending on if it is sparse or dense. When visualizing a graph of Facebook messages exchanged among users, we may want to encode each node with some additional information (such as the average number of messages a user sends per day). Now, such information may not be readily available in the data. But if we explore thoroughly, we may find a way to compute it from the data.